from unsloth.registry.registry import ModelInfo, ModelMeta, QuantType, _register_models

_IS_LLAMA_3_1_REGISTERED = False
_IS_LLAMA_3_2_REGISTERED = False
_IS_LLAMA_3_2_VISION_REGISTERED = False


class LlamaModelInfo(ModelInfo):
    @classmethod
    def construct_model_name(cls, base_name, version, size, quant_type, instruct_tag):
        key = f"{base_name}-{version}-{size}B"
        return super().construct_model_name(base_name, version, size, quant_type, instruct_tag, key)


class LlamaVisionModelInfo(ModelInfo):
    @classmethod
    def construct_model_name(cls, base_name, version, size, quant_type, instruct_tag):
        key = f"{base_name}-{version}-{size}B-Vision"
        return super().construct_model_name(base_name, version, size, quant_type, instruct_tag, key)


# Llama 3.1
LlamaMeta_3_1 = ModelMeta(
    org = "meta-llama",
    base_name = "Llama",
    instruct_tags = [None, "Instruct"],
    model_version = "3.1",
    model_sizes = ["8"],
    model_info_cls = LlamaModelInfo,
    is_multimodal = False,
    quant_types = [QuantType.NONE, QuantType.BNB, QuantType.UNSLOTH],
)

# Llama 3.2 Base Models
LlamaMeta_3_2_Base = ModelMeta(
    org = "meta-llama",
    base_name = "Llama",
    instruct_tags = [None],
    model_version = "3.2",
    model_sizes = ["1", "3"],
    model_info_cls = LlamaModelInfo,
    is_multimodal = False,
    quant_types = [QuantType.NONE, QuantType.BNB, QuantType.UNSLOTH],
)

# Llama 3.2 Instruction Tuned Models
LlamaMeta_3_2_Instruct = ModelMeta(
    org = "meta-llama",
    base_name = "Llama",
    instruct_tags = ["Instruct"],
    model_version = "3.2",
    model_sizes = ["1", "3"],
    model_info_cls = LlamaModelInfo,
    is_multimodal = False,
    quant_types = [QuantType.NONE, QuantType.BNB, QuantType.UNSLOTH, QuantType.GGUF],
)

# Llama 3.2 Vision
LlamaMeta_3_2_Vision = ModelMeta(
    org = "meta-llama",
    base_name = "Llama",
    instruct_tags = [None, "Instruct"],
    model_version = "3.2",
    model_sizes = ["11", "90"],
    model_info_cls = LlamaVisionModelInfo,
    is_multimodal = True,
    quant_types = {
        "11": [QuantType.NONE, QuantType.BNB, QuantType.UNSLOTH],
        "90": [QuantType.NONE],
    },
)


def register_llama_3_1_models(include_original_model: bool = False):
    global _IS_LLAMA_3_1_REGISTERED
    if _IS_LLAMA_3_1_REGISTERED:
        return
    _register_models(LlamaMeta_3_1, include_original_model = include_original_model)
    _IS_LLAMA_3_1_REGISTERED = True


def register_llama_3_2_models(include_original_model: bool = False):
    global _IS_LLAMA_3_2_REGISTERED
    if _IS_LLAMA_3_2_REGISTERED:
        return
    _register_models(LlamaMeta_3_2_Base, include_original_model = include_original_model)
    _register_models(LlamaMeta_3_2_Instruct, include_original_model = include_original_model)
    _IS_LLAMA_3_2_REGISTERED = True


def register_llama_3_2_vision_models(include_original_model: bool = False):
    global _IS_LLAMA_3_2_VISION_REGISTERED
    if _IS_LLAMA_3_2_VISION_REGISTERED:
        return
    _register_models(LlamaMeta_3_2_Vision, include_original_model = include_original_model)
    _IS_LLAMA_3_2_VISION_REGISTERED = True


def register_llama_models(include_original_model: bool = False):
    register_llama_3_1_models(include_original_model = include_original_model)
    register_llama_3_2_models(include_original_model = include_original_model)
    register_llama_3_2_vision_models(include_original_model = include_original_model)


if __name__ == "__main__":
    from unsloth.registry.registry import MODEL_REGISTRY, _check_model_info

    MODEL_REGISTRY.clear()

    register_llama_models(include_original_model = True)

    for model_id, model_info in MODEL_REGISTRY.items():
        model_info = _check_model_info(model_id)
        if model_info is None:
            print(f"\u2718 {model_id}")
        else:
            print(f"\u2713 {model_id}")
